2. Obtain PPPs for the benchmark countries by comparing the prices of each good and service.

3. Use capital city price surveys by United Nations International City Service Commission, Employment Conditions Abroad (a British firm), and the US State Department, to estimate PPPs for a wider range of countries.

4. By regressing PPPs obtained in step 2 on PPPs obtained in step 3 for the sample of benchmark countries, PPPs for non-benchmark countries are estimated based on their PPP estimates obtained in step 3.

5. Use PPPs to convert the countries' national currency expenditures (from national accounts) to a common currency unit.

Steps 1-5 were carried out for the base year (1985 for PWT version 5; 1996 for PWT version 6.1).

6. Real GDP per capita in PPP for other years is obtained by applying the growth rates from the constant-price national accounts series to the base-year real GDP per capita.

See pages 329, 341-4 of Robert Summers and Alan Heston (1991) "The Penn World Table (Mark 5): An Expanded Set of International Comparisons, 1950-1988" Quarterly Journal of Economics, 106, pp.327-368.

Sunday, February 3, 2019

Downloadable here. The spatial resolution is 30x30 arc-second (about 1x1 km) across the globe between 75 degrees north and 65 degrees south. Available annually since 1992 (and up to 2013, as of August 2016). The nighttime light intensity in each cell is represented by the "digital number", an integer from 0 to 63.

Storeygard (2016) uses light as a measure of city-level income across cities in Africa.

Bleakey and Lin (2012) use nighttime light as a measure of spatial distribution of contemporary economic activity, to see whether portage sites still predict where economic activities are concentrated today, long after their original advantage became obsolete.

Top-coding: The maximum value of light intensity is 63. This issue shouldn't matter much for poor and middle-income countries. Henderson et al. (2012) remove Singapore and Bahrain from their cross-country analysis for this concern (see footnote 16)

Compatibility across years and satellites: Satellite sensors age over time and are replaced periodically. Thus, the same digital number does not necessarily mean the same level of light intensity across years and satellites. Henderson et al. (2012) deal with this concern by controlling for year fixed effects in a regression of log GDP on log light per area.

Alternatively, the following book chapter attempts to calibrate values from different satellites to account for inter-satellite differences and inter-annual sensor decay:

To deblur the data with Abrahams's Matlab code, you need the pct_lights.tif files. Unfortunately, this file for 2011 is missing on the website. If you have downloaded and kept this file somewhere in your computer, let NOAA people know about it.

High latitude locations: Due to long daytime length, nighttime light cannot be observed in summer for high latitude locations (the raw satellite images are taken between 8:30 and 10:00 pm local time). For this reason, Henderson et al. (2012) exclude observations north of the Arctic Circle.

Validation as a measure of income/wealth

Logarithm of light intensity per area (and its long-run change over the 15-year period) is known to be linearly correlated with

Pinkovskiy and Sala-i-Martin (2016) (p. 609) calibrate the exponent on the digital number to match the average income of the states in Mexico (obtained from Luxembourg Income Study). They note (fn. 20), "We allow the calibrated exponent to differ across years, but in no year is it smaller than 5/2, and in some years it is as large as 9. Therefore, it is likely that the specification that is prevalent in the literature (setting the exponent equal to unity) is incorrect."

Validation as a measure of public goods provision

Michalopoulos and Papaioannou (2014) shows that logarithm of light intensity per area is correlated with access to electrification, presence of a sewage system, access to piped water, and education (averaged across households in each enumeration area) from Afrobarometer Surveys in 17 African countries.

Electrified villages are consistently brighter than unelectrified villages across a variety of nighttime satellite images

Electrified villages appear brighter in satellite imagery because of the presence of streetlights, and brightness increases with the number of streetlights.

The correlation between light output recorded by the satellite with household electricity use and access is low.

Min and Gaba (2014) conduct the same validation exercise for villages in Vietnam in 2013. They reach the same conclusions except for the last point: in Vietnam, household-level access to electricity is also correlated with nighttime light satellite images.

Baskaran et al (2015) also measure the proportion of villages with the positive value of nighttime light at the village centroid.

Storeygard (2016) measure the city-level light intensity as follows: first convert the original data "into one binary grid encoding whether a pixel was lit in at least one satellite-year. These ever-lit areas were then converted to polygons; contiguous ever-lit pixels were aggregated, and their DNs were summed within each satellite-year." (p. 1268)

Monday, December 17, 2018

Acemoglu and Robinson (2012), p. 412, cite it as the last census of Botswana asking questions about ethnicity. "In the Ngwato reserve, for example, only 20 percent of the population identified themselves as pure Ngwato; though there were other Tswana tribes present, there were also many non-Tswana groups whose first language was not Setswana."

We propose a summary statistic for the economic well-being of people in a country. Our measure incorporates consumption, leisure, mortality, and inequality, first for a narrow set of countries using detailed micro data, and then more broadly using multi-country datasets.